1. Field of the Invention
The present invention relates to a method and apparatus for removing noise in still and moving pictures, and the invention can be used in any still or moving picture compression or coding algorithm where the reconstructed picture suffers from block distortion noise commonly known as blocky noise. This method selectively filters the image such that the picture is restored and the blocky noise as well as ring and mosquito noise are removed.
2. Description of the Related Art
Many algorithms and standards exist for compression of still and moving pictures. These include standards such as the ITU-T H.261 and H.263, and the ISO standards for JPEG, MPEG-1 and MPEG-2. More recently, work has begun on the MPEG-4 video coding standard. All of the algorithms mentioned use the transform coding technique, in particular the Discrete Cosine Transform, DCT. In this technique the picture is first partitioned into small blocks of 8 pixels high and 8 pixels wide. These blocks of pixels are then transformed into the DCT domain where the transform coefficients are subjected to quantization to reduce the amount of information. At high compression ratios, most of these coefficients are quantized to zero. The quantization noise introduced during this process results in visual artifacts when the blocks of coefficients are transformed back into the pixel domain.
This artifact is a result of loss in the high frequency components of the transform coefficients. Since these coefficients are reduced to zero, the inverse quantization process cannot faithfully reproduce the original signal. The result is that the pixels at the edge of two adjacent DCT blocks have very different values. This difference in values makes the picture appear to be made up of blocks. This visual artifact is known as a blocky noise artifact. FIGS. 1A and 1B graphically depict how the blocky noise is created by coarse quantization.
FIG. 1A shows original pixel values in which pixels P.sub.0, P.sub.1, P.sub.2, P.sub.3, P.sub.4, P.sub.5, P.sub.6 and P.sub.7 have gradually increasing values. Note that a line between pixels P.sub.3 and P.sub.4 represent a boundary between the blocks for the DCT processing. Thus, the pixels P.sub.0, P.sub.1, P.sub.2, and P.sub.3 are processed in one DCT block, and pixels P.sub.4, P.sub.5, P.sub.6 and P.sub.7 are processed in the next neighboring DCT block. After DCT processing, the DCT coefficients are quantized. During the quantization, some data are lost. Then, after inverse DCT processing, the pixels P.sub.0, P.sub.1, P.sub.2, and P.sub.3 in the one DCT block will take different values from their original values, as shown in FIG. 1B. Similarly, the pixels P.sub.4, P.sub.5, P.sub.6 and P.sub.7 in the neighboring DCT block will take different values from their original values. In FIG. 1B, arrows added to the points representing the pixel values show the changes between before and after the DCT processing, quantization processing, and inverse DCT processing. After the inverse DCT processing, as shown in FIG. 1B, there will be a step between the pixel values P.sub.3 and P.sub.4, resulting in a loss of a gradual change of the pixel value at the boundary between the blocks for the DCT processing. Thus, the reproduced image will be a mosaic effect added picture.
FIGS. 2A and 2B show how the loss of high frequency coefficients in the DCT domain results in ring and mosquito noise artifacts. Note that in FIG. 2A, a dotted line between pixels P.sub.2 and P.sub.3 represent a discontinuity of the picture, such as an edge line of a door in the picture. After the DCT processing, quantization processing, and inverse DCT processing, the pixel values P.sub.2 and P.sub.3 will be further separated, as shown in FIG. 2B, resulting in a so-called, ring and mosquito noise providing undesirably emphasized edge lines in the picture.
The main problem to be solved is how to devise a general algorithm that can be applied to the picture with the artifact such that the blocky noise as well as ring and mosquito noise are reduced or removed. The goal is to manipulate the picture in such a way that the recovered picture is visually as close to the original picture as possible.
The second problem to be solved is how to prevent the blocky noise as well as ring and mosquito noise from propagating to the next frame in a sequence of moving pictures where motion compensation is used.